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I published another episode of AWS Bites podcast, the weekly show where we address interesting questions about AWS:

25. What can you do with Kinesis Data Streams?

Eoin and Luciano are back with some more AWS messaging services material! This time we talk about Kinesis Data Streams and everything there’s to know about them! In this episode of AWS Bites, we cover the difference between data streaming and queue or message buses. How Kinesis can let you process large batches of messages in near real time. Why you might use Kinesis as a queue or pub/sub bus in some cases. A few different ways to use Kinesis with the focus on avoiding too much complexity. We also provide some insights based on our experience with Kinesis Data Streams including real use cases, monitoring tips and some gotchas to be aware of.

00:00 Intro
01:06 What is data streaming?
03:24 Use cases for Kinesis Data Streams
07:00 Main features of Kinesis Data Streams
09:25 Limits with Kinesis Data Streams
13:10 How to scale Kinesis Data Streams
18:10 Enhanced fan out mode
20:10 Pricing
22:12 Observability and metrics
25:10 How to produce and consume messages
31:40 Processing Kinesis Data Streams with Lambda
36:10 How Kinesis integrates with other AWS services
39:35 Deep dive material
41:02 Outro

In this episode, we mentioned the following resources:
- Our introductory episode about what services you should use for events:
- Amazon Kinesis Producer Library (KPL):
- Amazon Kinesis Consumer Library (KCL):
- Kinesis Library wrapper for Node.js:
- Kinesis Library wrapper for Python:
- Kinesis Data Stream with captures from DynamoDB (for CDC):
- Kinesis Data Stream with captures from Aurora:
- Kinesis Data Analytics:
- Kinesis Firehose:
- Must-read on Kinesis: Anahit Pogosova’s two-part blog series: &
- Cloudonaut Kinesis vs MSK:
- Deep Dive on Lambda Consumers for Kinesis / Heeki Park - re:invent 2020

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